Spiking neural networks (SNNs) represent a class of neural networks that emulate the operation of biological neurons within the nervous system, simulating the transmission of electrical signals between neurons to facilitate information processing and learning. Deep SNNs possess the capability to extract intricate features from data, making them well-suited for classification tasks. Nonetheless, the complexity of deep SNNs and the multitude of hyperparameters pose challenges in effectively determining optimal parameters, often resulting in performance degradation. The Tree-structured Parzen Estimator (TPE) optimization algorithm is frequently employed to address global optimization problems for black-box models. This study leverages the Bayesian optimization algorithm to fine-tune the learning rate of SNNs and the membrane time constant of Leaky Integrate-and-Fire neurons within each layer. The optimized deep SNN models are subsequently utilized for event data classification. The experimental findings demonstrate a remarkable enhancement in the performance of deep SNNs following the application of the TPE optimization algorithm. Specifically, the network achieved an accuracy of 97.95% on the SED dataset, 76.03% on the CIFAR10-DVS dataset, and 96.87% on the DVS Gesture 128 dataset. This research underscores the potential of conventional optimization algorithms in optimizing tasks for SNNs, offering a novel approach to boosting the efficacy of spiking neural networks.